Mapping oilfield brine-contaminated sites with mid-spatial resolution remotely sensed data

Unger, Daniel; Bowes, Cindy; Farrish, Kenneth W.; Hung, I‐Kuai

doi:10.1080/15481603.2013.850252

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…At this stage, laboratory reflectance data obtained with the FieldSpec Pro FR-3 were resampled to the 357 bands of the ProSpecTIR-VS airborne hyperspectral sensor. Earlier studies have shown that PCA can be useful in studies of soil salinity, since the first component is usually associated with the brightness (average reflectance) of the samples, which increases with salt concentration [20][21][22]. It was therefore decided that PCA should be applied to the laboratory data resampled to the bands of the ProSpecTIR-VS. As the first derivative can enhance spectral features associated with salts, derivative analysis was applied to the resampled reflectance data, preceded by the use of a moving average filter with a three-band window.…”

Section: Ec and Spectral Reflectance Measurementsmentioning

confidence: 99%

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

et al. 2017

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Soil salinization due to irrigation affects agricultural productivity in the semi-arid region of Brazil. In this study, the performance of four computational models to estimate electrical conductivity (EC) (soil salinization) was evaluated using laboratory reflectance spectroscopy. To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)). Principal component analysis (PCA) and the first-order derivative analysis were applied to the data to generate metrics associated with soil brightness and spectral features, respectively. The three sets of data (reflectance, PCA, and derivative) were tested as input variable for Extreme Learning Machine (ELM), Ordinary Least Square regression (OLS), Partial Least Squares Regression (PLSR), and Multilayer Perceptron (MLP). Finally, the laboratory models were inverted to a ProSpecTIR-VS image (400-2500 nm) acquired with 1-m spatial resolution in the northeast of Brazil. The objective was to estimate EC over exposed soils detected using the Normalized Difference Vegetation Index (NDVI). The results showed that the predictive ability of the linear models and ELM was better than that of the MLP, as indicated by higher values of the coefficient of determination (R 2 ) and ratio of the performance to deviation (RPD), and lower values of the root mean square error (RMSE). Metrics associated with soil brightness (reflectance and PCA scores) were more efficient in detecting changes in the EC produced by soil salinization than metrics related to spectral features (derivative). When applied to the image, the PLSR model with reflectance had an RMSE of 1.22 dS·m −1 and an RPD of 2.21, and was more suitable for detecting salinization (10-20 dS·m −1 ) in exposed soils (NDVI < 0.30) than the other models. For all computational models, lower values of RMSE and higher values of RPD were observed for the narrowband-simulated sensors compared to the broadband-simulated instruments. The soil EC estimates improved from the RapidEye to the HRG and OLI spectral resolutions, showing the importance of shortwave intervals (SWIR-1 and SWIR-2) in detecting soil salinization when the reflectance of selected bands is used in data modelling.

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Section: Ec and Spectral Reflectance Measurementsmentioning

confidence: 99%

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

et al. 2017

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“…In this paper, we performed a first-derivative analysis to expand the modeling data. Previous studies have shown that the first principal component of spectral data increases with increasing salt content [26,27]. Principal component analysis allows the hyperspectral data to be downscaled to obtain the main features of the data and reduces the correlation between the components, which is beneficial for modeling; thus, in this study, the data were subjected to principal component analysis, and the top ten principal components were selected.…”

Section: Methodsmentioning

confidence: 99%

Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China

Wang

Zhang

Shen

et al. 2021

Water

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Soil salinity due to irrigation diversion affects regional agriculture, and the development of soil composition estimation models for the dynamic monitoring of regional salinity is important for salinity control. In this study, we evaluated the performance of hyperspectral data measured using an analytical spectral device (ASD) field spec standard-res hand-held spectrometer and satellite sensor visible shortwave infrared advanced hyperspectral imager (AHSI) in estimating the soil salt content (SSC). First derivative analysis (FDA) and principal component analysis (PCA) were applied to the data using the raw spectra (RS) to select the best model input data. We tested the ability of these three groups of data as input data for partial least squares regression (PLSR), principal component regression (PCR), and multiple linear regression (MLR). Finally, an estimation model of the SSC, Na+, Cl−, and SO42− contents was established using the best input data and modeling method, and a spatial distribution map of the soil composition content was drawn. The results show that the soil spectra obtained from the satellite hyperspectral data (AHSI) and laboratory spectral data (ASD) were consistent when the SSC was low, and as the SSC increased, the spectral curves of the ASD data showed little change in the curve characteristics, while the AHSI data showed more pronounced features, and this change was manifested in the AHSI images as darker pixels with a lower SSC and brighter pixels with a higher SSC. The AHSI data demonstrated a strong response to the change in SSC; therefore, the AHSI data had a greater advantage compared with the ASD data in estimating the soil salt content. In the modeling process, RS performed the best in estimating the SSC and Na+ content, with the R2 reaching 0.79 and 0.58, respectively, and obtaining low root mean squared error (RMSE) values. FDA and PCA performed the best in estimating Cl− and SO42−, while MLR outperformed PLSR and PCR in estimating the content of the soil components in the region. In addition, the hyperspectral camera data used in this study were very cost-effective and can potentially be used for the evaluation of soil salinization with a wide range and high accuracy, thus reducing the errors associated with the collection of individual samples using hand-held hyperspectral instruments.

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“…In situ data collection and frequent field monitoring of environmental analysis are gradually replaced with newer and more efficient methods, such as remote sensing (RS) and Geographical Information System (GIS) techniques. Unger et al [11] systematically applied RS techniques with extended area coverage and effectively modeled and evaluated environmental resources. Alexakis et al [12] integrated RS data and GIS analysis and demonstrated how these tools were used in landfill siting to better address environmental sustainability and support urban municipalities.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Latest Research Trends on the Use of Satellite Imagery in Solid Waste Disposal Applications from 2012 to 2021

Karimi

Mahmud

et al. 2023

Environments

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There is currently no review article on the role of remote sensing (RS) tools on waste disposal site (WDS) applications. Permanent waste disposal is the world’s most commonly used solid waste management method, and a specific review is warranted. To investigate research trends and to identify knowledge gaps on the use of satellite-based RS in WDS applications, 170 studies published over the last decade, from 2012 to 2021, were examined and classified using a bibliometric approach. Results are discussed with respect to relevancy, satellite types, study origins, RS analytical methods, and applications. Out of 72 short-listed studies, 44.4% were carried out in Asia, followed by Europe with 18.0%. Asia is also a leading region in the use of multiple satellite products. Only two satellite products were utilized in African studies. The absence of local satellites could potentially be the reason behind the sole use of global satellite imagery. Globally, Landsat contributed 70.8% of the total studies. Sentinel products represented only 8.3%. About 44% of the studies used various RS indices when addressing WDS-related issues. The majority of studies (56%) applied image classification methods to study changes in land use and land cover. The temporal trend reveals a general increase in the total number of studies, particularly for suitable site detection and disposal-site-induced anomaly detection. This review directly addresses the knowledge management aspect of data-driven solid waste management.

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Mapping oilfield brine-contaminated sites with mid-spatial resolution remotely sensed data

Cited by 4 publications

References 12 publications

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Estimation of Soil Salt and Ion Contents Based on Hyperspectral Remote Sensing Data: A Case Study of Baidunzi Basin, China

A Systematic Review of the Latest Research Trends on the Use of Satellite Imagery in Solid Waste Disposal Applications from 2012 to 2021

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